Amazon’s uses machine learning to drive product recommendations. They use a combination of Collaborative Filtering and Next-in-Sequence models to make predictions on goods an individual consumer may need next. Amazon possesses a massive database of consumer purchase behavior to power its predictions.

Amazon uses AI for the logistics side of the business. Artificial intelligence reroutes, changes delivery arrival times, and makes other adjustments for accuracy and efficiency. Soon, Amazon’s interest in drone delivery will start delivering to your doorstep.

And you can expect even more innovative from Amazon, especially in the AI arena.

Why?

Because Amazon’s patent on one-click payments is set to expire this year.

Losing the one feature that led the giant to domination won’t deter them. It will only feed the fire to find new ways to be disruptive.

Amazon is filing numerous machine learning and AI-focused patents.

Soon, personalized online experiences powered by artificial intelligence will be the expectation.

The Future of AI, Retail and ROI

After all, AI enables an ecommerce website to recommend products uniquely suited to shoppers and enables people to search for products using conversational language or images, as though they were interacting with a person.

This has been one of the key missing ingredients for a larger ecommerce revenue share within the retail industry: lack of the personalization brick-and-mortars can offer.

In that same vein, other opportunities emerging include using AI to personalize the customer journey.

This alone could be a huge value-add to online retailers.

Retailers that have implemented personalization strategies see sales gains of 6-10%, a rate two to three times faster than other retailers, according to a report by Boston Consulting Group (BCG).

It could also boost profitability rates by 59% in the wholesale and retail industries by 2035, according to Accenture.

With AI, there are many opportunities, but what does it all mean? And where do you start?

Embracing AI for ecommerce is no longer a feat of Amazon’s unparalleled resources.

In 2016, artificial intelligence was democratized as cloud-based microservices and made available for fractions of a penny per transaction.

The same disruptive forces powering Amazon, Google, Facebook, and Netflix is now democratized.

Right now, many businesses are getting ahead. Most, however, don’t know where or how to start.

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2. Expertise gaps.

Data scientist expertise, for example, is often under-appreciated or over-generalized.

Companies struggle maximizing on data projects by not having the correct expertise on the right project.

Expertise is in high demand.

In the United States, for instance, there were approximately 150 million workers in 2016, but only 235,000 data scientists. OR .15% of the working population for one of the most in-demand fields (MIT Sloan Management Review, 2017)

3. Bad data.

Artificial Intelligence output is only as great as it’s input.

If you don’t have the right quality or quantity of data, the value of AI is limited.

Most companies are committed to collecting and storing data but lack resources and understanding to identify the good from the bad.

The most valuable data for AI remains hidden in unstructured or flat files.

In fact, 80% of all data is unstructured while less than 1% is being analyzed today (IBM).

4. Competing technology priorities.

Companies are often unsure how to stack rank AI spend against other technology and information spending.

Often the CIO and CTO need to align and pool budgets to execute successful projects where data is captured, stored, accessed, and processed.

5. Unclear use-cases.

Next to “lacks a clear vision” the top barrier to AI adoption is the uncertainty around finding relevant use cases.

It is important to know what is easy and what’s hard in artificial intelligence today for online retailers like you.

It is critical to move from a passive state of AI exploration to an active state of piloting projects.

To close the thinking-doing gap:

Take a pragmatic approach.

Identify narrow use cases well-supported with data.

Choose open-source AI algorithms or companies offering SaaS products to quickly see success on small projects.

Learn what an AI win looks like and the process to go about creating them.

Let’s dive in and get started.

Leading Global Brands are Choosing Open SaaS

Make your retail site more flexible and innovative while also saving time, money, and launching your site faster.

3. Dynamic Pricing.

Dynamic pricing is a strategy based on which retailers change the price of the product based on supply and demand.

AI enabled dynamic pricing is a new disruptive force to hit the ecommerce world.

While having fluctuating prices are not new (happy hours, stock market, airline tickets), the data we can now access unlocks new potential.

We can now append customer data, competitive pricing data, and sales transaction data to predict when to discount, what to discount, and dynamically calculate the minimum amount of discount needed to ensure a transaction.

Dynamic pricing algorithms are an emerging field in artificial intelligence and ecommerce leaders may be hesitant to trust the models that inform pricing.

Early adopters are already leaving competitors behind.

Amazon is the current leader in applying dynamic pricing to their products. They are seeing massive success.

While other online retailers are experimenting with dynamic pricing, Amazon has it mastered.

It’s a key reason they are ousting competition. They’ve managed to price their commodities lower than others.

4. Artificial Agents.

Artificial agents and chatbots are a computer program designed to simulate conversations with human users, especially over the Internet.

Artificial agents are being used to interface with customers on ecommerce sites, inform customer service agents how to service inquiries, and even facilitate sales.

It is important to note that bots are not totally self-reliant. They are great tools to help facilitate simple transactions (like answer basic questions, set appointments, or triage) and provide basic decisions.

A human in the loop to act as a backup to the bot is still required to prevent user frustration.

The ecommerce giant eBay is a pioneer in the use of bots for commerce.

5. Predictive Behavior Modeling.

You need a successful sales and marketing strategy to support the engine.

And successful sales and marketing are predicated on a strong understanding of the customer.

Today we use our own experiences working with customers, past purchase behavior, market analysis, and personas to better understand how our customers may behave in the future.

Access to more data, sophisticated neural nets, and processing power is enabling ecommerce leaders to understand their customers and new trends in behavior better than ever.

Our ability to anticipate our customer next move is made possible by the predictive capabilities of AI.

Now, we can access a multitude of structured and unstructured data sources like social media, loyalty cards, sales, and market research to create deep psychographic profiles of our known customers to spot emerging trends, and predict unknown customers demographics.

You also need the right expertise on staff to derive the value hidden in your data.

And this can be the most challenging part. After all, data science is a misunderstood field.

As mentioned previously, many business and technical leaders over-generalize the data scientist skill set and often don’t have the right expertise on the correct projects. (For a helpful guide on the definitions of roles in the data science field, see here.)

Ensuring your staff has the right data expertise is critical to making data actionable.

Having the right strategy, team, and technology to support AI allows ecommerce companies to evolve from simply collecting data to predicting behavior and taking proactive measures.

Where should you begin?

Initially, partner with firms who provide part-time, or project-based consulting and execution for projects.

Then, staff internal team-members who can work alongside hired help to learn and document the process.

AI initiatives should start small but have a snowball effect.

Data, ideas, and products will be tightly integrated and provide a perpetual loop of innovation and advanced capabilities.

Over time, have a plan to develop your AI center of excellence, or competency. The partnership to pervasive model reduces risks and up-front expenditures but also provides sustainable competitive advantage moving forward.

How to Build Your Brand’s Ecommerce AI Muscle

To move from data to action, you need a starting point.

Here are the steps you should follow to build out an AI center of excellence and begin to strengthen your brand’s AI muscle.

Suppress the urge to post an opening for an AI expert to take this all on. And don’t just punt this over to your CIO or CTO.

Start with a strategy. Winning strategies take a practical approach and start small.

Find narrow use cases that are relevant to the overall corporate strategy. The most successful AI use cases live at the intersection of business objectives, data differentiation, and readily available artificial intelligence models.

Leverage third-party expertise on a project or part-time basis which can objectively help you build your strategy. Starting with a hire-first strategy may take 6 to 9 months to complete your AI vision. A tiger team of experts can help you build your AI roadmap in 6 weeks.

Once you build the roadmap, ID narrow use cases that solve the biggest problems with the quickest implementations scenarios. Build or implement an MVP version of the solution. An MVP (minimum viable product) should provide the opportunity to deploy your solution quickly and also provide an interface for training employees on the AI model to confidently execute its task.

Once confidence is achieved (both algorithmically and institutionally), build toward the full-scale solution.

A team of experts are running AI proof-of-concept development and execute the learning process for all AI models being deployed.

All of these components are carefully organized and managed through a carefully constructed process with DevOps.

For this reason, we are seeing the rise of the AI product manager.

The AI Product Manager takes the lead for any new initiatives requiring artificial intelligence.

They can work within individual business units to identify the needs and generate ideas on how artificial intelligence can be implemented.

The AI Product Manager then finds the data and models required to execute.

They may even build a business case for acquiring new data sources to enrich the algorithm’s output.

Over time, the AI Center of Excellence helps ecommerce driven organizations move beyond seeing project-level benefit to being truly disruptive with augmented intelligence and capabilities.

Executive Summary

Since 2016, ecommerce companies at large have begun to focus on AI as an augmented capability for recurring, manual tasks such as dynamic pricing, virtual agents, personalized experiences, and more efficient delivery.

For larger companies, like Amazon, AI is an incredibly differentiating force.

For smaller, up-and-coming brands, AI is a change management challenge.

But the gap between the AI haves and the AI have nots is widening.

The prospect of another seismic technology shift may be daunting, especially on the heels of the digital transformation.

Don’t be a victim of analysis or change paralysis.

Use the foundation laid from prior transformation initiatives and data collection as the wind beneath your sails.

Here is a quick checklist on how to tackle the AI in ecommerce for your brand.

Start small. Don’t worry about auditing the organization and hiring your first C-level AI leader out of the gate.

Find a good partner who can take you from data collection through to a well-defined strategy, and then into action.

Learn from that partner to build your own competency over time.

You’ve already accomplished the hard part. You have data and a corporate strategy.

All you need is a relevant, narrow use cases to apply to your first AI project.

AI implementation can be hard to get off the bench, but doing so is critical to survival in the future ecommerce economy where winners and losers will be defined by what they do with data, and how they scale their human capital.

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Steve is co-founder of KUNGFU.AI, a consultancy who helps companies build strategies and build solutions for artificial intelligence. Steve Meier has over 14 years working in business development, marketing, product management, and creative strategy. He believes in a future convergence of sales and marketing as a single organism whose mission is to help, educate, and consult. Steve's passion is in bleeding edge technologies and has worked extensively with IBM Watson, leading activations of Watson as a sales and marketing tool. Steve recently acquired a Certificate from MIT Sloan School of Management in Artificial Intelligence: Implications for Businesses Strategy.